Recent Advances in Research on Unmanned Aerial Vehicles

211 Pages·2013·5.51 MB·English

by  Fariba Fahroo

LLeecctture Notes in Control

and Information Sciences
444
Fariba Fahroo
Le Yi Wang
George Yin (Eds.)
Recent Advances in
Research on Unmanned
Aerial Vehicles
123
Lecture Notes
in Control and Information Sciences 444
Editors
ProfessorDr.-Ing.ManfredThoma
InstitutfuerRegelungstechnik,UniversitätHannover,Appelstr.11,30167Hannover,
Germany
E-mail:[email protected]
ProfessorDr.FrankAllgöwer
InstituteforSystemsTheoryandAutomaticControl,UniversityofStuttgart,
Pfaffenwaldring9,70550Stuttgart,Germany
E-mail:[email protected]
ProfessorDr.ManfredMorari
ETH/ETLI29,Physikstr.3,8092Zürich,Switzerland
E-mail:[email protected]
SeriesAdvisoryBoard
P.Fleming
UniversityofSheffield,UK
P.Kokotovic
UniversityofCalifornia,SantaBarbara,CA,USA
A.B.Kurzhanski
MoscowStateUniversity,Russia
H.Kwakernaak
UniversityofTwente,Enschede,TheNetherlands
A.Rantzer
LundInstituteofTechnology,Sweden
J.N.Tsitsiklis
MIT,Cambridge,MA,USA
Forfurthervolumes:
http://www.springer.com/series/642
Fariba Fahroo, Le Yi Wang, and George Yin (Eds.)
Recent Advances in
Research on Unmanned
Aerial Vehicles
ABC
Editors
FaribaFahroo GeorgeYin
AFOSR/RSL DepartmentofMathematics
ComputationalMathematics WayneStateUniversity
Arlington Detroit
Virginia Michigan
USA USA
LeYiWang
ECEDept.
WayneStateUniversity
Detroit
Michigan
USA
ISSN0170-8643 ISSN1610-7411 (electronic)
ISBN978-3-642-37693-1 ISBN978-3-642-37694-8 (eBook)
DOI10.1007/978-3-642-37694-8
SpringerHeidelbergNewYorkDordrechtLondon
LibraryofCongressControlNumber:2013935113
(cid:2)c Springer-VerlagBerlinHeidelberg2013
Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof
thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation,
broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation
storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology
nowknownorhereafterdeveloped.Exemptedfromthislegalreservationarebriefexcerptsinconnection
with reviews or scholarly analysis or material supplied specifically for the purpose of being entered
and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of
this publication or parts thereof is permitted only under the provisions of the Copyright Law of the
Publisher’slocation,initscurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer.
PermissionsforusemaybeobtainedthroughRightsLinkattheCopyrightClearanceCenter.Violations
areliabletoprosecutionundertherespectiveCopyrightLaw.
Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication
doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant
protectivelawsandregulationsandthereforefreeforgeneraluse.
Whiletheadviceandinformationinthisbookarebelievedtobetrueandaccurateatthedateofpub-
lication,neithertheauthorsnortheeditorsnorthepublishercanacceptanylegalresponsibilityforany
errorsoromissionsthatmaybemade.Thepublishermakesnowarranty,expressorimplied,withrespect
tothematerialcontainedherein.
Printedonacid-freepaper
SpringerispartofSpringerScience+BusinessMedia(www.springer.com)
Preface
Unmannedaerialvehicles(UAVs),alsoknownasunmannedaircraftsystems(UAS)
ordrones,haveseenanunprecedentedgrowthrecentlybothinmilitarymissionsand
innewdevelopmentstowardcivilandcommercialapplications.Althoughremotely
operated small UAVs are commercially available and their successful operations
in reconnaissance, combat operation, logistic support have been well recognized,
autonomouscontrolofUAVsasateamforacommonmissionremainsaverychal-
lengingtask.
A team of launched and coordinated UAVs requires advanced technologies in
sensing,communication,computing,andcontrolto improvetheirintelligenceand
robustness towards autonomousoperations.At present, capabilities for command,
control,communications,computing,intelligence,surveillance,andreconnaissance
ofnetworkedUAVsareinadequatewhichrestrictpotentialapplicationsofUAVs.To
enhancereliability,robustness,andmissioncapabilityofateamofUAVs,asystem-
orientedandholisticapproachisdesirableinwhichallcomponentsandsubsystems
areconsideredintermsoftheirrolesinandimpactontheentiresystem.
Thisvolumeaimstosummarizetherecentprogress,identifychallengesandop-
portunities,anddevelopnewmethodologiesandsystemsoncoordinatedUAVcon-
trol.Agroupofexpertsworkinginthisareahavecontributedtothisvolumeinsev-
eraldiversifiedbutrelatedaspectsofautonomouscontrolofnetworkedUAVs.Their
papersintroducenewcontrolmethodologies,algorithms,andsystemsthataddress
severalimportantissuesindevelopingintelligent,autonomousorsemi-autonomous,
networked systems for the nextgeneration of UAVs. The papers share a common
focusonimprovedcoordinationofthemembersofthenetworkedsystemtoaccom-
plishacommonmission,toachieveheightenedcapabilityinsystemreconfiguration
tocompensateforlostmembersorconnections,andtoenhancerobustnessagainst
terraincomplicationsandattacks.
In their paper, Fariba Fahroo and Michael Ross present the theoretical frame-
work for Pseudospectral (PS) optimal control theory that can be used for design
ofnewmaneuversforUAVsthatservetheneedsofthemission.Giventheexpan-
sion of the operationalenvelopefor agile UAVs, design of these maneuversin an
VI Preface
optimalmannerhasbecomefeasibleandtheuseofadvancednumericaltechniques
for these design problems is essential. In this paper, the authors explore the the-
oretical (convergence)and implementationissues of these PS controlsonboardof
UAVs.
BenG.FitzpatrickconsiderscertainidempotentmodificationsofBayesiananal-
ysiswhicharesuitablefordecisionmakinginmixedinitiativeandmulti-agentsys-
temsinhighlyautonomousoperations.ThepaperexaminesBayesiannetworksand
Bayesian knowledgebases within the contextof max-plusprobability.Then these
techniquesareusedtotreatdecentralizedcontrolofunmannedairsensingassets.
ThepaperbySeungHakHanandWilliamM.McEneaneydealswiththeprob-
lemwhereonewishestointerceptavehiclebeforeitreachesaspecifictarget.The
vehicletravelsonaroadnetwork.Thereareunattendedgroundsensors(UGSs)on
theroadnetwork.Thedata obtainedbya UGSisuploadedto anunmannedaerial
vehicle (UAV) when the UAV overflies the UGS. There is an interceptor vehicle
which may travel the road network. The interceptor uses intruder state estimates
based on the data uploaded from the UGSs to the UAV. There is a negative pay-
offiftheintruderisinterceptedpriortoreachingthetargetlocation,andapositive
payoffiftheintruderreachesthetargetlocationwithouthavingbeenpreviouslyin-
tercepted.TheintruderpositionismodeledasaMarkovchain.Theobservationsare
corruptedbyrandomnoise.Adynamicprogrammingapproachistakenwherethe
value is propagated using a min-plus curse-of-dimensionality-freealgorithm. The
double-descriptionmethodforpolyhedraisusedtoreducecomputationalloads.
Laura R. Humphrey’s paper explores the use of model checking for verifica-
tion in several UAV related applications, including a centralized cooperative con-
trol scheme, a decentralizedcooperativecontrolscheme, and a scenario involving
ahumanoperator.Therehasbeenincreasedinterestsinverificationtechniquesfor
complex,autonomoussystems.TheteammightincludeseveralautonomousUAVs
andunattendedgroundsensors,aswellasahumanoperator.Insuchcases,thereis
aneedfortechniquestoverifytheaggregatebehavioroftheteam.Currentresearch
suggests that standard software modeling and verification techniques can be ex-
tendedtomeetthisneed.Dependingonwhetherteamcoordinationisimplemented
in acentralizedordecentralizedmanner,behaviorofa cooperativecontrolsystem
orbehaviorof the individualagentscanbe modeledusinga formalmodelinglan-
guage, system specifications can be expressed using a modal logic, and a model
checkercanbeusedtoverifythatthemodeledbehavioroftheteammeetsallspec-
ifications. Specificationsare written in linear temporallogic. Examplemodelsare
codedinPROMELA,whichareverifiedusingthemodelcheckerSPIN.
To determineoptimalpolicies for largescale controlledMarkovchains, K. Kr-
ishnamoorthy, M. Park, S. Darbha, M. Pachter, and P. Chandler consider a base
perimeterpatrolstochasticcontrolproblem.To determinetheoptimalcontrolpol-
icy, one has to solve a Markov decision problem, whose large size renders ex-
act dynamic programming methods intractable. They propose a state aggregation
based approximate linear programming method to construct provably good sub-
optimalpoliciesinstead.Thestatespaceispartitionedandtheoptimalcost-to-goor
valuefunctionisapproximatedbyaconstantovereachpartition.Byminimizinga
Preface VII
non-negativecost functiondefinedon the partitions,one can constructan approx-
imate value function that is an upper bound for the optimal value function of the
originalMarkovchain.Theyshowthatthisapproximatevaluefunctionisindepen-
dentofthenon-negativecostfunction(orstatedependentweights;asitisreferred
tointheliterature)andmoreover,thisistheleastupperboundthatonecanobtain,
given the partitions. Furthermore,we show that the restricted system of linear in-
equalitiesalsoembedsafamilyofMarkovchainsoflowerdimension,oneofwhich
canbeusedtoconstructatightlowerboundontheoptimalvaluefunction.Ingen-
eral, the construction of the lower bound requires the solution to a combinatorial
problem.
Khanh Pham’s paper considers a class of distributed stochastic systems where
interconnectedsystemscloselykeeptrackofreferencesignalsissuedbyacoordina-
tor.Muchoftheexistingliteratureconcentratesonconductingdecisionsandcontrol
synthesis based solely on expected utilities and averaged performance. However,
research in psychology and behavioral decision theory suggests that performance
risk playsan importantrolein shapingpreferencesin decisionsunderuncertainty.
A new equilibrium concept, called “person-by-person equilibrium” for local best
responses,is proposedto analyzesignalingeffectsand mutualinfluencesbetween
anincumbentsystem,itscoordinatorandimmediateneighbors.Individualmember
objectivesaredefinedbythemulti-attributeutilityfunctionsthatcapturebothper-
formance expectation and risk measures to model the satisfaction associated with
localbestresponseswithrisk-averseattitudes.Theproblemclass andapproachof
coordinationcontrolofdistributedstochasticsystemsproposedhereareapplicable
toandexemplifiedinmilitaryorganizationsandflexiblyautonomoussystems.
InthepaperofLeYiWangandG.Yin,ateamofUAVsinsurveillanceoperations
aimstoachievefastdeployments,robustnessagainstuncertaintiesandadversaries,
and adaptability when the team expands or reduces under time-varying and local
communicationconnections.ThispaperintroducesanewframeworkforUAVcon-
trolbasedontheemergingconsensuscontrolfornetworkedsystems.Duetounique
featuresofUAVtasks,theconsensuscontrolproblembecomesweightedandcon-
strained,beyondthetypicalconsensusformulation.Usingonlyneighborhoodcom-
municationsamongUAVmembers,theteamcoordinationachievesaglobaldesired
deployment.Algorithmsareintroducedandtheirconvergencepropertiesareestab-
lished.ItisshownthatthealgorithmsachieveasymptoticallytheCrame´r-Raolower
bound, and hence is optimal among all algorithms in terms of MS errors. Perfor-
manceoptimizationwithinnetworktopologyconstraintsisfurtherexplored,includ-
ing optimal convergencerates and optimalrobustness. Examplesand case studies
demonstrateconvergence,robustness,andscalabilityofthealgorithms.
Thisbookiswrittenforresearchers,engineers,practitioners,andstudentsinUAV
researchandrelatedfields.Selectedmaterialsfromthebookmayalsobeusedina
graduateseminarcourseonspecialtopicsinUAVs.
Without the help of many individuals, the book project would not have been
completed.First,wewouldliketothankalltheauthorsfortheircontributions.This
bookprojectwas initiated duringseveral visits of GeorgeYin and Le Yi Wang to
the Air Force Research Laboratory,Wright-PattersonAir Force Base. Our special
VIII Preface
thanksgotoDr.SivaBandaforprovidinguswiththeopportunityandforconnecting
us with the researchers in the Control Science Center of Excellence, Air Vehicles
Directorate. Our thanks also go to the Editor of the Series for providing us with
anexcellentforumandoutlettopresentourresults.Finally,wethanktheSpringer
professionalsforfinalizingthebook.
Arlington,Virginia FaribaFahroo
Detroit,Michigan LeYiWang
Detroit,Michigan GeorgeYin
February2013
Contents
Preface ....................................................... V
1 Enhancing the PracticalAgilityofUAVs viaPseudospectral
OptimalControl ........................................... 1
FaribaFahroo,I.MichaelRoss
2 MaxPlusDecisionProcessesinPlanningProblemsforUnmanned
AirVehicleTeams .......................................... 31
BenG.Fitzpatrick
3 SolutionofanOptimalSensingandInterceptionProblemUsing
IdempotentMethods........................................ 47
SeungHakHan,WilliamM.McEneaney
4 ModelCheckingforVerificationinUAVCooperativeControl
Applications............................................... 69
LauraR.Humphrey
5 ApproximateDynamicProgrammingAppliedtoUAVPerimeter
Patrol .................................................... 119
K.Krishnamoorthy,M.Park,S.Darbha,M.Pachter,P.Chandler
6 A Framework for Coordinationin Distributed Stochastic
Systems:PerfectStateFeedbackandPerformanceRiskAversion .. 147
KhanhPham
7 WeightedandConstrainedConsensusControlwithPerformance
OptimizationforRobustDistributedUAV Deploymentswith
DynamicInformationNetworks .............................. 181
LeYiWang,GeorgeYin
AuthorIndex ..................................................... 207
Chapter 1
Enhancing the Practical Agility of UAVs via
Pseudospectral Optimal Control
FaribaFahrooandI.MichaelRoss
Abstract.UAVshavethecapabilitytoperformmaneuversthatwouldotherwisebe
unrealistic to do in an inhabited craft due to human limitations of both response
timeaswellascomfortorblack-outlimits.Giventhisexpansionofitsoperational
envelope, a natural question to ask is on the design of new maneuvers for UAVs
thatservetheneedsofthemission.Oneoftheseneedsisarapidresponsetime.In
thiscontext,weexplorethedesignofaminimum-timevelocityreversalmaneuver
forafixed-wingUAV.Pseudospectraloptimalcontroltheoryisusedtoaddressthis
problem.We discuss a wide-rangeof issues fromtheoryto flightimplementation.
We showthatthese issues areinterdependentandexplorekeyconvergenceresults
that are critical for a successful flight. Results for a MONARC UAV are used to
illustratethecloseconnectionbetweentheoryandpractice.
1.1 Introduction
The agility of UAVs can be characterized in terms of solutions to minimum-time
optimal control problems. A flight implementationof solutions to these problems
requirescarefulconsiderationofthemismatchbetweenthemodel(usedinsolving
theoptimalcontrolproblem)andtheactualvehicledynamicsaswellasconsidera-
tionsoftheinteractionsbetweentheinner-loopandouterloop.Whenthesepracti-
calconsiderationsareproperlytakenintoaccount,theresultingproblemistypically
nonlinear,constrained in state and controlspaces with possible interdependencies
betweenthesespaces[1].
FaribaFahroo
AirForceOfficeofScientificResearch(AFOSR),Arlington,VA
e-mail:[email protected]
I.MichaelRoss
DepartmentofMechanicalandAerospaceEngineering,NavalPostgraduateSchool,
Monterey,CA93943
e-mail:[email protected]
F.Fahrooetal.(Eds.):RecentAdv.inRes.onUnmannedAerialVeh.,LNCIS444,pp.1–30.
DOI:10.1007/978-3-642-37694-8_1 (cid:2)c Springer-VerlagBerlinHeidelberg2013